The proliferation of social media opened doors for massive collection of data for sentiment analysis [1]}. These are important channels of human communication, allowing for instantaneous spread of information. Twitter emerged as one focal point to capture and analyze data, in which users express their opinions, feelings and thoughts regarding entities or events. Detecting sentiments in Twitter differs from detecting sentiments in conventional text such as blogs and forums, due to the reduced size of the textual data, and because of the information context, addition of symbols in the form of emojis and irony and subjectivity. However, social media networks, such as Twitter, also provide the opportunity to congregate textual data with more information, usually in the form of images, videos or audio. The coupling of multiple data sources, allows the development of multimodal classification, in which a method leverages more than one type of data to perform classification [2]}. This is significantly harder in the context of sentiment analysis, as extracting sentiments solely from textual information is easier than combining information from text and, for example, images. Even though a multimodal approach can improve the performance when compared to a sole text-approach [3]}, this is a challenging task, especially when using data acquired from social media, as the different data types are sparse and can have different contexts, present irony, different intentions and their combined evaluation is not trivial.
